Following the works of Negoita et al. [1]}, [2]} and Jiang et al. [3]}, in this paper we employ a feed-forward ANN to extrapolate, at large model spaces, the hypernuclear NCSM results of Refs. [4]}, [5]}, [6]} for the
\(\Lambda \)  separation energies \(B_\Lambda \)  (defined as the difference between the binding energies of the hypernucleus and the corresponding core nucleus) of the lightest hypernuclei, \(^3_\Lambda \) H, \(^4_\Lambda \) H and \(^4_\Lambda \) He, obtained with chiral nucleon-nucleon [7]} and nucleon-nucleon-nucleon [8]} interactions at N\(^3\) LO and N\(^2\) LO, respectively, and hyperon-nucleon [9]} potentials at LO. We find that an ANN with a single hidden layer of eight neurons is sufficient to extrapolate correctly the \(\Lambda \)  separation energies of the three hypernuclei considered. This is in agreement with the universal approximation theorem [10]}, [11]}, [12]} which assures that any continuous function can be realized by a neural network with just one hidden layer.
